A k-Nearest Neighbors Approach for COCOMO Calibration

被引：0

作者：

Le, Phu ^{[1
]}

Vu Nguyen ^{[2
]}

机构：

[1] Hitachi Consulting, Global Cybersoft JSC Vietnam, Helios Bldg,Dist 12, Hcm City, Vietnam

[2] Vietnam Natl Univ HCM City, Univ Sci, Fac Informat Technol, 227 Nguyen Van Cu St,Ward 4,Dist 5, Hcm City, Vietnam

来源：

2017 4TH NAFOSTED CONFERENCE ON INFORMATION AND COMPUTER SCIENCE (NICS) | 2017年

关键词：

Software cost estimation; COCOMO; calibration; linear regression with constraints; optimization; linear programming; K-Nearest Neighbor; COST ESTIMATION; MODELS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating them produce favorable estimation accuracy. The best estimates can be produced using a small number of nearest neighboring projects to calibrate. We recommend that organizations use between 8 and 10 nearest projects to calibrate the COCOMO model.

引用

页码：219 / 224

页数：6

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